Weight for It: Resistance Training Mitigates White Matter Hyperintensity-Related Disruption to Functional Networks in Older Females
Bibliographic record
Abstract
BACKGROUND: White matter hyperintensities (WMH) are associated with impaired cognition and increased falls risk. Resistance training (RT) is a promising intervention to reduce WMH progression, improve executive functions, and reduce falls. However, the underlying neurobiological process by which RT improves executive functions and falls risk remain unclear. We hypothesized that: 1) RT reduces the level of WMH-related disruption to functional networks; and 2) reduced disruption to the sensorimotor and attention networks will be associated with improved executive function and reduced falls risk. OBJECTIVE: Investigate the impact of 52 weeks of RT on WMH-related disruption to functional networks. METHODS: Thirty-two older females (65-75 years) were included in this exploratory analysis of a 52-week randomized controlled trial. Participants received either twice-weekly RT or balance and tone training (control). We used lesion network mapping to assess changes in WMH-related disruption to the sensorimotor, dorsal attention, and ventral attention networks. Executive function was measured using the Stroop Colour-Word Test. Falls risk was assessed using the Physiological Profile Assessment (PPA) and the foam sway test. RESULTS: RT significantly reduced the level of WMH-related disruption to the sensorimotor network (p = 0.012). Reduced disruption to the dorsal attention network was associated with improvements in Stroop performance (r = 0.527, p = 0.030). Reduced disruption to the ventral attention network was associated with reduced PPA score (r = 0.485, p = 0.049)Conclusion:RT may be a promising intervention to mitigate WMH-related disruption to the sensorimotor network. Additionally, reducing disruption to the dorsal and ventral attention networks may contribute to improved executive function and reduced falls risk respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".